ANTONIO
TRISTAN VEGA
PROFESORES TITULARES DE UNIVERSIDAD
![Foto de Carl-Fredrik](/img/nophoto.png)
Carl-Fredrik
Westin
Publicacións nas que colabora con Carl-Fredrik Westin (12)
2016
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Adjugate Diffusion Tensors for Geodesic Tractography in White Matter
Journal of Mathematical Imaging and Vision, Vol. 54, Núm. 1, pp. 1-14
2015
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The DTI Challenge: Toward Standardized Evaluation of Diffusion Tensor Imaging Tractography for Neurosurgery
Journal of Neuroimaging, Vol. 25, Núm. 6, pp. 875-882
2014
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A novel Riemannian metric for geodesic tractography in DTI
Mathematics and Visualization
2012
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Deblurring of probabilistic ODFs in quantitative diffusion MRI
Proceedings - International Symposium on Biomedical Imaging
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Efficient and robust nonlocal means denoising of MR data based on salient features matching
Computer Methods and Programs in Biomedicine, Vol. 105, Núm. 2, pp. 131-144
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Least squares for diffusion tensor estimation revisited: Propagation of uncertainty with Rician and non-Rician signals
NeuroImage, Vol. 59, Núm. 4, pp. 4032-4043
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Optimal real-time estimation in diffusion tensor imaging
Magnetic Resonance Imaging, Vol. 30, Núm. 4, pp. 506-517
2011
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Probabilistic ODF estimation from reduced HARDI data with sparse regularization
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
2010
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A new methodology for the estimation of fiber populations in the white matter of the brain with the Funk-Radon transform
NeuroImage, Vol. 49, Núm. 2, pp. 1301-1315
2009
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Bias of least squares approaches for diffusion tensor estimation from array coils in DT-MRI
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
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Estimation of fiber Orientation Probability Density Functions in High Angular Resolution Diffusion Imaging
NeuroImage, Vol. 47, Núm. 2, pp. 638-650
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On the blurring of the Funk-Radon transform in Q-ball imaging
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)